'''simple docstring'''






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     Optional[int]         =       {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "gptj"
   __lowerCAmelCase         =   {
       "max_position_embeddings": "n_positions",
       "hidden_size": "n_embd",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }
   def __init__(      self      :			str, UpperCamelCase__      :			Optional[int]=5_04_00, UpperCamelCase__      :			Tuple=20_48, UpperCamelCase__      :			int=40_96, UpperCamelCase__      :			str=28, UpperCamelCase__      :			int=16, UpperCamelCase__      :			Tuple=64, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			List[str]="gelu_new", UpperCamelCase__      :			List[Any]=0.0, UpperCamelCase__      :			Optional[int]=0.0, UpperCamelCase__      :			Tuple=0.0, UpperCamelCase__      :			Optional[int]=1e-5, UpperCamelCase__      :			int=0.02, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			int=5_02_56, UpperCamelCase__      :			str=5_02_56, UpperCamelCase__      :			str=False, **UpperCamelCase__      :			str, )  ->			Optional[int]:
        _A    		=  vocab_size
        _A    		=  n_positions
        _A    		=  n_embd
        _A    		=  n_layer
        _A    		=  n_head
        _A    		=  n_inner
        _A    		=  rotary_dim
        _A    		=  activation_function
        _A    		=  resid_pdrop
        _A    		=  embd_pdrop
        _A    		=  attn_pdrop
        _A    		=  layer_norm_epsilon
        _A    		=  initializer_range
        _A    		=  use_cache

        _A    		=  bos_token_id
        _A    		=  eos_token_id

        super().__init__(
            bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, tie_word_embeddings=UpperCamelCase__, **UpperCamelCase__     )







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[int], UpperCamelCase__      :			PretrainedConfig, UpperCamelCase__      :			str = "default", UpperCamelCase__      :			List[PatchingSpec] = None, UpperCamelCase__      :			bool = False, )  ->			Any:
        super().__init__(UpperCamelCase__, task=UpperCamelCase__, patching_specs=UpperCamelCase__, use_past=UpperCamelCase__     )
        if not getattr(self._config, 'pad_token_id', UpperCamelCase__     ):
             # TODO: how to do that better?
             _A    		=  0
   @property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Mapping[str, Mapping[int, str]]:
        _A    		=  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}     )
        if self.use_past:
             self.fill_with_past_key_values_(UpperCamelCase__, direction='inputs'     )
             _A    		=  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}

        return common_inputs
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			int:
        return self._config.n_layer
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        return self._config.n_head
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			PreTrainedTokenizer, UpperCamelCase__      :			int = -1, UpperCamelCase__      :			int = -1, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			Optional[TensorType] = None, )  ->			Mapping[str, Any]:
        _A    		=  super(UpperCamelCase__, self     ).generate_dummy_inputs(
            UpperCamelCase__, batch_size=UpperCamelCase__, seq_length=UpperCamelCase__, is_pair=UpperCamelCase__, framework=UpperCamelCase__     )

        # We need to order the input in the way they appears in the forward()
        _A    		=  OrderedDict({'input_ids': common_inputs['input_ids']}     )

        # Need to add the past_keys
        if self.use_past:
             if not is_torch_available():
                  raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
             else:
                  import torch

                  _A		,       _A    		=  common_inputs['input_ids'].shape
                  # Not using the same length for past_key_values
                  _A    		=  seqlen + 2
                  _A    		=  (
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  _A    		=  [
                      (torch.zeros(UpperCamelCase__     ), torch.zeros(UpperCamelCase__     )) for _ in range(self.num_layers     )
                  ]

        _A    		=  common_inputs['attention_mask']
        if self.use_past:
             _A    		=  ordered_inputs['attention_mask'].dtype
             _A    		=  torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(UpperCamelCase__, UpperCamelCase__, dtype=UpperCamelCase__     )], dim=1     )

        return ordered_inputs

   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        return 13







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




